Hyperinsulinemia Drives Diet-Induced Obesity Independently of Brain Insulin Production

Cell Metabolism

Article

Hyperinsulinemia Drives Diet-Induced ObesityIndependently of Brain Insulin ProductionArya E. Mehran,1,2 Nicole M. Templeman,1,2 G. Stefano Brigidi,1 Gareth E. Lim,1,2 Kwan-Yi Chu,1,2 Xiaoke Hu,1,2

Jose Diego Botezelli,1,2 Ali Asadi,1,2 Bradford G. Hoffman,2 Timothy J. Kieffer,1,2 Shernaz X. Bamji,1 Susanne M. Clee,1

and James D. Johnson1,2,*1Department of Cellular and Physiological Sciences2Department of Surgery

University of British Columbia, Vancouver, BC V6T 1Z3, Canada

*Correspondence: [email protected]

http://dx.doi.org/10.1016/j.cmet.2012.10.019

SUMMARY

Hyperinsulinemia is associated with obesity andpancreatic islet hyperplasia, but whether insulincauses these phenomena or is a compensatoryresponse has remained unsettled for decades. Weexamined the role of insulin hypersecretion in diet-induced obesity by varying the pancreas-specificIns1 gene dosage in mice lacking Ins2 gene expres-sion in the pancreas, thymus, and brain. Age-depen-dent increases in fasting insulin and b cell masswere absent in Ins1+/�:Ins2�/� mice fed a high-fatdiet when compared to Ins1+/+:Ins2�/� littermatecontrols. Remarkably, Ins1+/�:Ins2�/� mice werecompletely protected from diet-induced obesity.Genetic prevention of chronic hyperinsulinemia inthis model reprogrammed white adipose tissue toexpress uncoupling protein 1 and increase energyexpenditure. Normalization of adipocyte size andactivation of energy expenditure genes in whiteadipose tissue was associated with reduced inflam-mation, reduced fatty acid spillover, and reducedhepatic steatosis. Thus, we provide genetic evidencethat pathological circulating hyperinsulinemia drivesdiet-induced obesity and its complications.

INTRODUCTION

Obesity is a worldwide epidemic with increasing prevalence

and is a major independent risk factor for heart disease, hyper-

tension, stroke, diabetes, cancer, and many other diseases

(Despres and Lemieux, 2006; Olshansky et al., 2005). Themolec-

ular causes of obesity remain largely unexplained, despite the

identification of common gene variants that contribute modest

risk (Frayling et al., 2007). Obesity research has benefited from

animal models in which genetic and environmental factors can

be manipulated in ways that are impossible with humans. For

example, invertebrates with reduced insulin or insulin signaling

have leaner, smaller bodies, along with increased life span

(Broughton et al., 2008; Li et al., 2003). Studies in mammals point

to cell type-specific roles for the insulin receptor in diet-induced

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obesity. Fat-specific insulin receptor knockout mice are pro-

tected from diet-induced obesity and have extended longevity

(Bluher et al., 2002; Katic et al., 2007), whereas elimination of

insulin receptors from some, but not all, brain regions increases

food intake and obesity (Bruning et al., 2000; Konner and Brun-

ing, 2012). The interpretation of studies involving insulin receptor

ablation is confounded by compensation between tissues and

disruption of insulin-like growth factor signaling involving hybrid

receptors (Bluher et al., 2002; Kitamura et al., 2003). Moreover,

knockout of the insulin receptor, by definition, induces tissue-

specific insulin resistance, which alone may promote hypergly-

cemia and insulin hypersecretion (Bruning et al., 1998; Kitamura

et al., 2003). Previous studies have been unable to determine the

role of hyperinsulinemia on obesity in the absence of insulin

resistance and glucose intolerance.

It is a widely held view that hyperinsulinemia simply represents

a compensation for systemic insulin resistance. Nevertheless,

the cause-and-effect relationships between hyperinsulinemia,

insulin resistance, obesity, and type 2 diabetes remain unre-

solved. Reports of elevated insulin secretion at birth, and of

insulin hypersecretion preceding obesity or insulin resistance,

are inconsistent with hyperinsulinemia being merely an adaptive

response (Genuth et al., 1971; Gray et al., 2010; Ishikawa et al.,

1998; Kitamura et al., 2003; Koopmans et al., 1997; Le Stunff and

Bougneres, 1994; Lustig, 2006; Odeleye et al., 1997; Shanik

et al., 2008; Zimmet et al., 1991). Furthermore, blocking hyperin-

sulinemia with drugs including diazoxide or octreotide can cause

weight loss in humans (Alemzadeh et al., 1998; Lustig et al.,

2006), although interpretation of these studies is complicated

by direct effects of these treatments on multiple tissues, in-

cluding white fat (Shi et al., 1999) and brain (Pocai et al., 2005).

Thus, although circumstantial evidence points to a role for hyper-

insulinemia in the development of obesity, to date, there has

been no direct evidence that circulating insulin itself drives

obesity in mammals. To directly test this hypothesis, one would

ideally genetically lower the amount of pancreatic insulin avail-

able without crossing the threshold that would result in diabetes.

Members of the insulin gene family are evolutionarily con-

served products of neurosecretory cells (Broughton et al.,

2008; Li et al., 2003). In mammals, insulin is recognized for its

critical metabolic roles, whereas the insulin-like growth factors

are most often associated with tissue growth. Evidence from

our group and others points to important growth factor roles of

insulin (Bluher et al., 2002; Johnson and Alejandro, 2008; Okada

etabolism 16, 723–737, December 5, 2012 ª2012 Elsevier Inc. 723

mailto:[email protected]


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Insulin Gene Dosage Controls Mammalian Obesity

724 Cell Metabolism 16, 723–737, December 5, 2012 ª2012 Elsevier Inc.

Cell Metabolism


et al., 2007). Mice and rats have two insulin genes, producing

hormones with similar peptide sequences. Deletion of both

rodent insulin genes causes death from neonatal diabetes (Duvil-

lie et al., 1997). It has been reported that ablation of either Ins1 or

Ins2 from the mouse genome does not result in significant

changes in glucose homeostasis in young mice fed a normal

diet, implying compensation or redundancy with regards to

metabolism (Duvillie et al., 1997; Leroux et al., 2001). However,

there is evidence that the two rodent insulin genes have specific

tissue distribution patterns suggesting some nonoverlapping

functions (Kojima et al., 2004; Moore et al., 2001; Pugliese

et al., 1997). Most studies have shown that Ins1 is restricted to

pancreatic b cells, where it contributes to approximately one-

third of the expressed and secreted insulin (Hay and Docherty,

2006). Ins2 is the ancestral gene, with gene structure, parental

imprinting, and a broad tissue distribution similar to human INS

(Deltour et al., 1993; Hay and Docherty, 2006).

In the present study, we took advantage of the pancreas-

specific expression of the murine Ins1 gene to demonstrate

that diet-induced pancreatic insulin hypersecretion promotes

obesity and its associated complications in this model. Mice

made genetically incapable of high-fat diet-induced fasting hy-

perinsulinemia had increased levels of Ucp1 in white adipose

tissue and increased energy expenditure. These results have

profound implications for our understanding of the role of insulin

in obesity and reveal unappreciated in vivo connections between

fasting hyperinsulinemia and adipocyte reprogramming. As the

human INS gene is subject to genetic variation (Le Stunff et al.,

2001), our results suggest a mechanism for establishing obesity

susceptibility to dietary stress.

RESULTS

Ins2, but Not Ins1, Is Expressed in the Central NervousSystemWhile it is clear that pancreatic b cells are the only significant

source of circulating hormonal insulin, it is known that some cells

outside the pancreas express modest amounts of Ins2/INS. For

example, Ins2 production in the thymus is widely accepted to

play a role in immune tolerance (Fan et al., 2010; Pugliese et al.,

1997). Over 80 reports have suggested that insulin can be

produced in the brain (de la Monte and Wands, 2008; Deltour

Figure 1. Central Nervous System Expression of Ins2, but Not Ins1

(A) Analysis of Ins2 and Ins1 mRNA expression (relative to the control gene) in th

C57Bl6/J mice.

(B) No significant compensatory upregulation of either insulin gene in Ins1�/�:Ins2the qRT-PCR. Further validation of the specificity of mouse Ins1 and Ins2 Taqman

found in the Supplemental Information.

(C) Ins1 and Ins2 gene expression assessed by Taqman real-time qPCR in arrayed

postnatal brain.

(D) Distribution of neurons expressing insulin protein in the hippocampus of 12-we

antibodies were validated with Ins1�/�:Ins2+/+ and Ins1+/+:Ins2�/� mice (see the

(E) Close-up view of individual insulin neurons in the rostral cortex.

(F) Colocalization of insulin protein in cell bodies and axons (green), with b-gala

neurons (i), anterior olfactory nucleus neurons (ii), and cerebellar Purkinje neuron

(G) Confocal imaging of morphologically identified neurons showing punctate

endoplasmic reticulum (ER; green; iii) and in the somas of 7 day in vitro cultured ra

EGFP transfection was used to outline the boundaries andmorphological health of

neurons and staining was absent in neurons from Ins1�/�:Ins2�/� mice (see the

See also Figures S1 and S2.

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et al., 1993; Devaskar et al., 1993; Giddings et al., 1985; Havran-

kova et al., 1978; Lamotte et al., 2004;Wicksteed et al., 2010) (see

the Supplemental Information available online). Deep-

sequencing data from over 200 tissue libraries revealed broad

expression of Ins2, but not Ins1, in multiple regions of the devel-

opingandpostnatal brain (FigureS1A). Theobservation of central

nervous system (CNS) Ins2 prompted us to employ Taqman

quantitative PCR (qPCR) primers, validated in Ins2�/� or Ins1�/�

islets (Figure S2A), to elucidate the expression pattern of insulin

in the brain. We observed brain expression of Ins2 messenger

RNA (mRNA), significantly above the background seen in Ins2�/�

brain tissue (Figures 1A and 1B). Ins1mRNAwas absent accord-

ing to these criteria. Using an array with complementary DNA

(cDNA) from multiple brain regions, we found Ins2 was broadly

expressed (most prominently in the hippocampus), while Ins1

was virtually absent (Figure 1C). Consistent with a local role for

neuronal insulin, quantitative analysis (Figure S2B) confirmed

that Ins2 expression in hippocampal neurons is much lower

than in pancreatic islets, which must deliver insulin to the entire

body. A similar RT-qPCR analysis of human brain cDNA array

confirmed INS expression in several regions, including hippo-

campus (Figure S1B). Chromatin immunoprecipitation with

antibodies to methylated histones, marking regions of active

transcription, followed by direct sequencing pointed to active

transcription around the Ins2 gene, but not the Ins1 gene, in

cerebral cortex and cerebellum (Figure S1C). Both genes had

activity marks in pancreatic islets (Figure S1C). Interestingly,

the active regions near the Ins2 locus inbrain appear tobedistinct

from those in islets. These observations are consistent withmany

reports that Ins2 promoters, but not Ins1 promoters, exhibit

robust activity in the brain (e.g., Wicksteed et al., 2010).

We studied the localization of insulin protein throughout the

brain using antibodies validated in Ins2�/� or Ins1�/� islets and

Ins1�/�:Ins2�/� neurons (Figure S2C–S2E). Specific insulin im-

munoreactivity and C-peptide immunoreactivity were observed

in the hippocampus, anterior olfactory nucleus, cerebral cortex,

cerebellar Purkinje neurons, and other discrete nuclei (Figures

1D–1G). We also took advantage of the fact that the Ins2�/�

mice used in our studies had LacZ knocked into in their endog-

enous Ins2 locus (Figure S2E). We found that the same adult

neurons that were positive for bGal also stained robustly for

insulin protein in Ins1�/�:Ins2+/�(bgal) mice (Figure 1F). To rule

e hypothalamus, posterior brain, and anterior brain of 6-month-old wild-type

+/+ or Ins1+/+:Ins2�/� mice. These mice also provide ideal negative controls for

real-time qPCR primer/probe sets in Ins1 or Ins2 knockout mouse islets can be

cDNA frommultiple murine brain regions at multiple stages of developing and

ek-old mice (i). Hippocampal section stained without primary antibody (ii). The

Supplemental Information).

ctosidase knocked into the endogenous Ins2 locus (nucleus; red) in cortical

s (iii) from 12-week-old Ins1�/�:Ins2+/�(bGal) mice.

and reticular C-peptide-2 immunoreactivity (red; ii) within calnexin-positive

t neonatal hippocampal neurons (approximately half of the cells were positive).

individual neurons (i). Identical results were observedwithmouse hippocampal

Supplemental Information). Error bars represent standard error of the mean.


B

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50

100

150

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Ins1

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Ins1+/+:Ins2-/- Control Diet Ins1+/-:Ins2-/- Control Diet Ins1+/+:Ins2-/- High Fat Diet Ins1+/-:Ins2-/- High Fat Diet

Control Diet

Control Diet

High Fat Diet

High Fat Diet

0

50

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Cell Metabolism



Cell Metabolism


out the possibility that insulin immunoreactivity arose from insulin

taken up into specific neurons via endocytosis, we cultured

hippocampal neurons in defined insulin-free media. Using an

antibody that recognizes mouse C-peptide 2, but not mouse

C-peptide 1 (Figure S2C), we identified endoplasmic reticulum

(ER) staining in cultured neurons (Figure 1G). The diffuse staining

pattern of insulin distribution, also observed with an antibody to

fully processed mature insulin, suggests a constitutive release

expected for a local trophic factor. Together, these data demon-

strate that Ins2 is produced in themammalian brain by a subset of

neurons, a feature conserved from lower animals (Rulifson et al.,

2002). Previous reports of insulin produced in the brain were

highly controversial because it was difficult to distinguish low

levels of insulin from background signals in the absence of ideal

negative controls (Ins2�/� and/or Ins1�/�mice) and positive con-

trols [Ins1�/�:Ins2+/�(bgal) mice]. The differential expression of two

murine insulin genesand the availability of Ins1and Ins2 knockout

mice presented a unique opportunity to specifically dissect the

role of circulating Ins1 derived from the pancreas.

Reduced Ins1 Prevents Diet-Induced b Cell Growth andFasting HyperinsulinemiaTo test the hypothesis that peripheral, pancreas-derived insulin

drives diet-induced obesity, we endeavored to selectively lower

circulating insulin by generating mice null for the brain-ex-

pressed Ins2 gene while modulating the gene dosage of

pancreas-specific Ins1 (Figure 2A). It was critical to first test

whether removal of one Ins1 allele in Ins2�/� mice would result

in a proportional reduction in Ins1 mRNA and circulating insulin.

Indeed, Ins1+/�:Ins2�/� mice exhibited an �50% reduction in

islet Ins1 mRNA compared to Ins1+/+:Ins2�/� mice (Figure 2B).

Analysis of insulin secretion and insulin content in islets in young

mice revealed some posttranscriptional compensation (Figures

2C and S4). However, by 1 year, insulin immunostaining, fasting

circulating insulin, and glucose-stimulated insulin secretion were

all markedly decreased in mice with reduced Ins1 gene dosage

(Figures 2D, 2I, and 3A).

Although the Ins1+/+:Ins2�/� control mice used in these exper-

iments have been reported to be indistinguishable from wild-

type mice on a control diet (Duvillie et al., 1997; Leroux et al.,

2001), it was essential to determine whether they would respond

to a high-fat diet with a similar hyperinsulinemia and b cell mass

increase seen in other control strains (Okada et al., 2007). Rela-

tive to chow-fed controls, Ins1+/+:Ins2�/�mice fed a 58% fat diet

since weaning developed age-dependent, high-fat diet-induced

fasting hyperinsulinemia by 7 weeks of age (Figures 2I and S4A).

Figure 2. Reduced Insulin Gene Dosage Prevents b Cell Expansion an

(A) Experimental design to test the role of circulating insulin on diet-induced obe

(B) qRT-PCR analysis of Ins1 mRNA in islets from 12-week-old mice (n = 3–5).

(C) Islet insulin content from 30 hand picked islets from 8-week-old mice (n = 3).

(D) Representative staining using anti-insulin (green) and anti-glucagon (red) an

evidence of islet autoimmunity.

(E) Ins1+/�:Ins2�/� mice failed to increase b cell mass in response to a high-fat d

(F) Cell death was assessed by measuring the number of TUNEL-positive b cells

(G and H) Proliferation was estimated by quantification of the percentage of PCNA

of PCNA immunoblot in islets isolated from 12-week-old mice (n = 4–6).

(I) Age-dependent high-fat diet-induced fasting hyperinsulinemia was prevented

*Significant difference between high-fat diet (HFD) mice (p < 0.05). **Significant dif

See also Figures S3 and S4.

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Persistent hyperinsulinemia was dependent on the expression of

both Ins1 alleles.

The fasting hyperinsulinemia in high fat-fed Ins1+/+:Ins2�/�

mice was associated with an �2 fold increase in fractional

b cell area, compared with mice fed a control diet (Figure 2E).

However, in high fat-fed Ins1+/�:Ins2�/� mice, b cell area was

equivalent to control diet levels, pointing to a critical role for basal

circulating insulin in this process (Figure 2E). Analysis of pro-

grammed cell death and proliferation in b cells from Ins1+/�:Ins2�/� mice suggested that circulating insulin had gene

dosage-dependent antiapoptotic and proproliferative autocrine

effects in the context of a high-fat diet (Figures 2F–2H). These

observations complement studies showing that mice lacking

insulin receptors on their b cells fail to increase b cell mass due

to defective proliferation and increased apoptosis (Okada

et al., 2007). Thus, long-term diet-induced fasting hyperinsuline-

mia was prevented in Ins1+/�:Ins2�/�mice (Figure 2I), which was

associated with a lack of diet-induced b cell expansion (Fig-

ure 2E). In other words, we generated mice that were genetically

incapable of high-fat diet-induced fasting hyperinsulinemia. We

were unable to observe high-fat diet-induced hyperinsulinemia

or high-fat diet-induced obesity in female Ins2�/� mice regard-

less of their Ins1 gene dosage (Figure S3). Male mice were

used for all subsequent studies designed to examine the effects

of diet on obesity.

Glucose Homeostasis in Mice with Reduced InsulinGene DosageSomewhat surprisingly, we observed only transient differences

in fasting glucose between Ins1+/�:Ins2�/� and Ins1+/+:Ins2�/�

mice, despite a modest reduction in glucose-stimulated insulin

release (Figures 3A, 3B, and S4B). Significant worsening of

glucose homeostasis in Ins1+/�:Ins2�/� mice was only observed

in a small subset of high fat-fedmice (twomice) during the period

of rapid somatic growth around 11 weeks of age and was not

observed later in life or on the chow diet (Figure 3C). Insulin

sensitivity was generally similar between the genotypes and

diets (Figure 3D). Therefore, comparing Ins1+/+:Ins2�/� mice

and Ins1+/�:Ins2�/� mice enabled us to test the effects of genet-

ically reduced pancreatic insulin secretion on obesity, in the

absence of sustained hyperglycemia or insulin resistance.

Reduced Ins1 Gene Dosage Prevents Diet-InducedObesityWe directly tested the hypothesis that pancreatic insulin hyper-

secretion is required for diet-induced obesity by tracking body

d Sustained Hyperinsulinemia on a High-Fat Diet

sity in the absence of Ins2.

tibodies in pancreas tissue sections from 12-month-old mice. We found no

iet (n = 3).

in pancreatic sections from 8-week-old mice (n = 3-5).

-positive b cells in pancreata from 8-week-old mice (n = 3) and by performance

in Ins1+/�:Ins2�/� mice (n = 6–16).

ference between control diet (CD)mice (p < 0.05). Error bars represent the SEM.


Figure 3. Glucose Homeostasis and Insulin Sensitivity in Mice with Reduced Insulin Gene Dosage

(A) Insulin release in response to intraperitoneal injection of 18% glucose in 53-week-old mice.

(B) Four-hour fasted glucose levels measured weekly over the first year of life.

(C) Intraperitoneal glucose tolerance was impaired in young, but not old Ins1+/�:Ins2�/� mice. Insets show the area under the curve (AUC).

(D) Insulin tolerance was statistically similar between all groups at all ages studied. Insets show the area over the curve (AOC).

All data are represented as means of at least five male mice in each group at all time points. *Statistical significance (p < 0.05). Error bars represent the SEM. See

also Figures S3 and S4.

Cell Metabolism


weight of Ins1+/+:Ins2�/� mice and Ins1+/�:Ins2�/� mice, fed a

control diet or an obesigenic high-fat diet (Figure 4A). We ob-

served robust adult-onset obesity in male high fat-fed Ins1+/+:

Ins2�/� control mice with all the expected hallmarks, including

increased fat-to-lean ratio measured by nuclear magnetic reso-

nance (NMR), increased fat pad weight, increased size of indi-

vidual adipocytes, and increased inflammation markers in white

adipose tissue (Figure 4). In striking contrast, Ins1+/�:Ins2�/�

728 Cell Metabolism 16, 723–737, December 5, 2012 ª2012 Elsevier

mice were completely protected from diet-induced adult-onset

weight gain for the duration of these 1-year-long experiments

(Figure 4A). The high-fat diet-induced increase in epididymal

fat pad weight was completely prevented (Figure 4B), resulting

in reduced circulating leptin (Figure 4C). Whole-body growth,

measured by tibia length, was independent of Ins1 gene dosage

(Figure 4D). Weights of other organs were not statistically

different between any of the groups (Figure 4E). Even at ages

Inc.

Cell Metabolism


prior to the significant divergence of body weight between high

fat-fed groups, fasting insulin and body weight were positively

correlated between mice within genotypes (Figure S4). Together

with the results above, these data suggest that circulating Ins1 is

an adipocyte-specific and b cell-specific growth factor.

Next, we sought to determine the physiological mechanisms

accounting for the lack of high-fat diet-induced adiposity in the

nonhyperinsulinemic Ins1+/�:Ins2�/� mice (Figures 4F and 4G).

Using indirect calorimetry, we found increased energy expendi-

ture preceding the onset of differences in body weight (Fig-

ure 4H). The timing of this observation was important because

the normalization of energy expenditure between mice with dif-

ferent levels of adiposity is complicated (Himms-Hagen, 1997).

Notably, we did not observe significant differences in food

intake, physical activity, or respiratory quotient (Figures 4I–4L).

Collectively, these in vivo data suggest that high-fat diet-induced

hyperinsulinemia promotes adult adipocyte hypertrophy and

nutrient storage. The loss of one Ins1 allele was sufficient to

increase energy expenditure and prevent adiposity in the context

of a high-fat diet.

Ins1 Is a Negative Regulator of Uncoupling, Lipolytic,and Inflammatory Genes in White FatWe predicted changes in energy expenditure and fat cell growth

over this long time scale would be associated with stable alter-

ations in gene expression patterns and cellular reprogramming,

rather than only acute signaling events. Indeed, while hyperinsu-

linemic Ins1+/+:Ins2�/� mice exhibited marked adipocyte hyper-

trophy, white adipose tissue from high fat-fed Ins1+/�:Ins2�/�

mice appeared similar to that from mice fed a control diet

(Figure 5A). To determine the molecular mechanisms underlying

the protective effects of reduced circulating insulin, we designed

a Taqman real-time PCR miniarray of 45 key metabolic, inflam-

matory, and insulin target genes (see the Supplemental Informa-

tion for the rationale behind each gene choice). In white adipose

tissue, there were few differences between the two groups of

mice on the low-fat diet, although insulin receptor mRNA was

significantly decreased in Ins1+/� mice (Figure 5B). In keeping

with the lean phenotype and increased energy expenditure, we

observed a gene expression pattern associated with energy

expenditure and lipid mobilization, including increased expres-

sion of uncoupling proteins and Pnpla2 (adipose triglyceride

lipase), in white adipose tissue from high fat-fed Ins1+/�:Ins2�/�mice versus high fat-fed Ins1+/+:Ins2�/�mice (Figure 5B).

The increase in Ucp1 gene expression in white adipose tissue

isolated from high fat-fed Ins1+/�:Ins2�/� mice suggested brown

adipose tissue-like features similar to what have been observed

in other lean models (Leonardsson et al., 2004). Ppargc1a

(Pgc1a) and Pparg, positive regulators of Ucp1 and energy

expenditure, were also upregulated at the mRNA level in white

adipose tissue from high fat-fed Ins1+/�:Ins2�/� mice. Further-

more, we observed downregulation of factors previously shown

to promote obesity and adipocyte differentiation in the white

adipose of high fat-fed Ins1+/�:Ins2�/�mice, including the insulin

target Egr2 (Krox20) and Nrip1 (RIP140), a corepressor that

suppresses Ucp1 expression in white adipose tissue (Leonards-

son et al., 2004) (Figure 5B). Interestingly, none of the uncoupling

proteins were differentially expressed between brown adipose

tissue of high fat-fed Ins1+/�:Ins2�/� mice and high fat-fed

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Ins1+/+:Ins2�/� mice (Figure S5A). We observed a tendency for

a gene expression profile promoting energy expenditure in skel-

etal muscle from high fat-fed Ins1+/�:Ins2�/� mice, although

these effects were not statistically significant with this sample

size (Figure S5B).

We selected a few differentially expressed genes with known

roles in insulin signaling and/or adipocyte differentiation for addi-

tional analysis at the protein level. Immunocytochemistry

showed what appeared to be enhanced Ucp1 protein in white

adipose tissue from high fat-fed Ins1+/�:Ins2�/� mice compared

with high fat-fed Ins1+/+:Ins2�/� littermates (Figure 5C). A signif-

icant increase in Ucp1 protein levels in high fat-fed Ins1+/�:Ins2�/� mice was confirmed and quantified by immunoblot of

white adipose tissue (Figure 5D). We also observed elevated

protein levels of Pparg and Srebf1/SREBP-1c (Figure 5E), point-

ing to the upstream transcriptional control of this process by

insulin. Immunoblot also confirmed a significant decrease in

Egr2 protein in high fat-fed Ins1+/�:Ins2�/� mice (Figure 5F), mir-

roring the effects on its mRNA and suggesting a possible ‘‘dedif-

ferentiation’’ of white adipocytes. These quantitative data clearly

demonstrate that the pancreas-specific Ins1 gene, and by exten-

sion the circulating insulin hormone, influences the expression of

a large number of key metabolic genes in white adipose tissue,

specifically in the context of a high-fat diet.

It is well established that obesity and insulin resistance are

associated with chronic low-grade inflammation of multiple

tissues, including white fat. However, the cause-and-effect rela-

tionships between these phenomena and hyperinsulinemia have

not been fully established. Our qRT-PCR analysis revealed a

broad reduction in inflammatory markers, including Tnfa and

Emr1 (F4/80; macrophages) in white adipose tissue from high

fat-fed Ins1+/�:Ins2�/� mice compared with high fat-fed Ins1+/+:

Ins2�/� littermates. These data suggest that insulin regulates

adipose inflammation either directly (e.g., bymodulating immune

cells), or indirectly (e.g., via the effects on adipocyte size/stress).

Reduced Pancreatic Ins1 Protects Mice from LipidSpillover and Fatty LiverIns1+/�:Ins2�/� mice were protected from elevated circulating

free fatty acids when compared to their high fat-fed Ins1+/+:

Ins2�/� littermate controls (Figure 6A). In accordance with the

lack of lipid spillover, livers of high fat-fed Ins1+/�:Ins2�/� mice

were protected fromhigh-fat diet-induced hepatic steatosis (Fig-

ure 6B) and associated stress pathway activation (Figure 6C).

The increase in liver triglyceride content in high fat-fed control

Ins1+/+:Ins2�/� mice was significantly reduced in Ins1+/�:Ins2�/�

mice, while significant differences in cholesterol were not

observed (Figures 6D and 6E). qRT-PCR analysis of liver showed

that most of the differences were related to diet (reduced Ddit3,

Pparg, Ptpn1, Il6, Igfbp1, Egr1, and Egr2; increased Glut4,

Srebf1, Ppargc1a, and Pck1), rather than between genotypes

on the high-fat diet (Figure 6C). There were also notable differ-

ences in some inflammation markers in bulk liver tissue (Fig-

ure 6C). The reduction in the stress marker Atf3 (Figure 6C)

prompted us to evaluate markers of oxidative stress and lipid

peroxidation in this tissue, but the levels of protein carbonyl

and 4-hydroxynonenal (HNE) were not different between high

fat-fed Ins1+/�:Ins2�/�mice and their high fat-fed Ins1+/+:Ins2�/�

littermates (Figure 6C). Our data support a paradigm whereby


10

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Ins1+/+:Ins2-/- HFD Ins1+/-:Ins2-/- HFD

Figure 4. Mice with Reduced Fasting Insulin Are Protected from High Fat-Induced Obesity as a Result of Increased Energy Expenditure

(A) Body weight tracked over 1 year, in multiple independent cohorts (assessed over several years; n = 5–11).

(B) Epididymal fat pad weight in 1-year-old mice (n = 5–11).

(C) Circulating leptin levels (n = 3).

(D and E) Tibial length and organ weight were measured at 1 year as indexes of somatic growth (n = 5–11).

(F and G) NMR spectroscopy (n = 3).

Cell Metabolism



Cell Metabolism


high fat consumption leads to chronic basal insulin hypersecre-

tion (perhaps via direct insulinotropic effects of fatty acids),

which then increases adipocyte size and lipid accumulation in

adipose tissue. The spillover of free fatty acids subsequently

leads to steatosis and ER stress in the liver and other tissues,

which may eventually result in pathway- and/or tissue-specific

insulin resistance when combined with additional factors (Fig-

ure 7). The combination of these factors, initiated by hyperinsu-

linemia-induced obesity, could predispose individuals to type 2

diabetes.

DISCUSSION

Pancreatic Insulin Hypersecretion Drives Fat Storageand Prevents Fat Burning in AdipocytesA canon of diabetes and obesity research is that obesity-associ-

ated insulin resistance causes hyperinsulinemia because the

pancreatic b cells are hyperstimulated to release more insulin,

although the physiological mechanism for this hyperstimulation

remains unclear since it often occurs prior to hyperglycemia.

Our data demonstrate that prevention of high-fat diet-induced

hyperinsulinemia through partial ablation of the pancreas-

specific Ins1 gene protects mice from diet-induced obesity

and associated complications by upregulating genes that mobi-

lize lipids and increase mitochondrial uncoupling while downre-

gulating genes required for differentiation in white adipose

tissue. This would be expected to produce small amounts of

heat at the expense of maximal calorie storage in white fat.

Specifically, we propose a model for diet-induced obesity

whereby hyperinsulinemia normally negatively regulates white

adipose tissue Ucp1 expression, via a gene network involving

Pparg and Nrip1, to suppress energy expenditure (Figure 7).

This idea is consistent with the general anabolic role for insulin

and the observation that reducing circulating insulin with diazo-

xide promotes weight loss in obese mice via an increase in basal

metabolic rate (Alemzadeh et al., 2008). Our in vivo data place

the pancreas-specific Ins1 gene upstream of obesity, and estab-

lish the causality of circulating hyperinsulinemia in adult fat

growth in the absence of insulin resistance (Figure 7A). In our

study, increased basal insulin secretion occurred without sus-

tained hyperglycemia. Indeed, both insulin resistance and insulin

hypersecretion can occur in normoglycemic humans, preceding

obesity and insulin resistance (Genuth et al., 1971; Gray et al.,

2010; Ishikawa et al., 1998; Kitamura et al., 2003; Koopmans

et al., 1997; Le Stunff et al., 2001; Odeleye et al., 1997). It is

notable that early hyperinsulinemia was the strongest predictor

of type 2 diabetes in a 24 year study (Dankner et al., 2009). Insulin

resistance and obesity can also be uncoupled in human lipody-

strophies (Hegele, 2003) and in many mouse models (Bruning

et al., 1998; Chen et al., 2010; El-Haschimi et al., 2003; Vijay-Ku-

mar et al., 2010; Wang et al., 2010). Further evidence for the

concept that hyperinsulinemia can be a primary factor in the

metabolic syndrome in humans can be found in the increased

risk of childhood weight gain observed in individuals with

(H and I) Energy expenditure was measured by indirect calorimetry in high fat-fe

gain (n = 5).

(J–L) We did not note significant differences in activity, food intake, or respirator

*Statistical significance (p < 0.05). Error bars represent the SEM. See also Figure

Cell M

elevated pancreatic insulin production caused by inheritance

of class I alleles of the INS gene (Le Stunff et al., 2001). Genetics

and epigenetics likely combine with environmental and dietary

factors that stimulate insulin hypersecretion early in life,

including in utero, to promote fat growth and eventually compli-

cations including type 2 diabetes. The therapeutic potential of

drugs that inhibit circulating insulin has previously been demon-

strated (Alemzadeh et al., 2008; Alemzadeh et al., 1998; Lustig

et al., 2006), but here we provide insight into the molecular

mechanism of these clinical observations. Our study provides

additional rationale for dietary and therapeutic efforts to partially

block insulin hypersecretion or peripheral insulin action

in specific tissues to combat obesity. However, insulin levels

must always be high enough to sustain glucose homeostasis.

Diet-Induced b Cell Expansion Is Regulated by InsulinIn VivoElegant studies have suggested that insulin receptor signaling

modulates postnatal b cell mass expansion in response to

high-fat diet (Okada et al., 2007), but this concept has remained

controversial, in part due to the ability of insulin receptor manip-

ulations to influence IGF1 signaling and to cause compensatory

changes in other tissues. The lack of b cell mass ‘‘compensa-

tion’’ in Ins1+/�:Ins2�/� mice provides direct in vivo evidence

that insulin itself is critical for b cell growth and survival under

conditions of nutrient stress. Importantly, our results demon-

strate that b cell proliferation is induced in the absence of sus-

tained hyperglycemia in vivo. In the past, we have used in vitro

cultures of dispersed primary islet cells, a system where insulin

and glucose can be clamped independently of each other, to

demonstrate that insulin, but not glucose, stimulates b cell prolif-

eration (Beith et al., 2008; Johnson et al., 2006). We have also

shown that glucose-stimulated ERK activation is proportionally

reduced in islets with reduced insulin gene dosage or treated

with somatostatin to block insulin exocytosis (Alejandro et al.,

2010; Alejandro et al., 2011), strongly suggesting that glucose

modulates b cell fate mainly via local autocrine/paracrine insulin

signaling. Indeed, >80% of the effects of glucose on b cell gene

expression are lost in b cells with insulin receptor knockdown

(Ohsugi et al., 2005). Together, many lines of evidence from

multiple groups support the concept that insulin acts via insulin

receptors to mediate the compensatory increase in b cell mass

and basal insulin release in the context of high-fat diet. This

has implications for efforts to increase b cell mass in both type

1 and type 2 diabetes.

Tissue-Specific Roles for Ins1 and Ins2: Relevance toHuman INSInsulin is the most studied hormone in biology, yet the present

study provides much needed integrated insight into its localiza-

tion and physiological functions in vivo. Clinically, insulin insuffi-

ciency resulting from a loss of greater than �80% of functional

b cell mass results in diabetes, promoting the prevailing mindset

that insulin’s roles are almost exclusively positive. Here we show

d mice at 20 weeks of age (green star in A), just prior to differences in weight

y quotient (n = 3–5).

s S3 and S4.


Figure 5. Molecular Mechanisms Leading to Protection from Diet-Induced Obesity in Ins1+/–:Ins2–/– Mice

(A) Hematoxylin and eosin-stained epididymal fat pad (n = 5–11).

(B) Taqman real-time qPCR quantification of 45 genes in epididymal white adipose tissue from 1-year-old mice. Results are sorted according to the magnitude of

the difference between high fat-fed Ins1+/�:Ins2�/�mice and high fat-fed Ins1+/+:Ins2�/� littermates (#p < 0.05). Geneswith a large negative relative expression are

colored red; genes with increased expression are green. $Significant difference between diets in the Ins1+/�:Ins2�/�mice. &Significant difference between diets in

the Ins1+/+:Ins2�/� mice. *Significant difference between genotypes on the control diet. Data are from 1-year-old mice (n = 5–11), unless otherwise specified.

(C) Ucp1 immunocytochemistry in white adipose tissue from 1-year-oldmice. The inset shows results from the identical staining protocol on brown adipose tissue

sections.

Cell Metabolism



Cell Metabolism


that insulin hypersecretion from b cells can be maladaptive.

Similarly, elegant studies in flies and worms have demonstrated

that deleting insulin genes, or blocking elements of the insulin

signaling pathway, dramatically increases life span and prevents

diseases associated with adiposity (Broughton et al., 2008; Li

et al., 2003). These model systems also clearly indicate that indi-

vidual insulin-like peptide genes have distinct functions despite

signaling through a single receptor (Broughton et al., 2008; Li

et al., 2003). In rodents, the Ins1 and Ins2 genes are under

partially distinct regulation (Alejandro et al., 2011; Hay and Doch-

erty, 2006). Previously, it has also been shown that the mouse

Ins1 and Ins2 genes have opposing roles on type 1 diabetes inci-

dence in theNODmouse due to the induction of thymic tolerance

by Ins2 (Fan et al., 2009; Moriyama et al., 2003), but the possi-

bility that they have distinct roles in obesity had not been

examined.

In the present study, we elucidated the tissue-specific

patterns of Ins1 and Ins2 expression. Usingmultiple approaches,

we found that Ins2 is present in CNS neurons, at both the mRNA

and protein levels. Uniquely, we present both negative and posi-

tive controls, arguing against qPCR or staining artifacts. We also

find distinct active chromatin marks on the Ins2 gene in the CNS

and provide RNA sequencing data. Given the conservation

between the rodent Ins2 gene and the human INS gene, we

were not surprised to find additional evidence for insulin produc-

tion in the human brain. In both species, we find robust insulin

expression in the hippocampus, pointing to potential roles in

learning and memory. It is interesting to note the recent interest

in clinical trials using nasal insulin to replace this putative central

neurotrophic factor for the treatment of Alzheimer’s disease. A

complete understanding of the role of Ins2 produced in the

CNS will require brain-specific Ins2 knockout mice.

We predict that insulin produced locally in specific brain

regions will have potent effects on food intake (Hallschmid

et al., 2012). Based on recent studies, we expect that some

insulin circuits will promote food intake, while others will

suppress it, and we predict that distinct populations of insulin

neurons will be differentially susceptible to insulin resistance

(Konner and Bruning, 2012; Rother et al., 2012). It should be

noted that our data do not address what may be an important

role of peripheral insulin acting on the brain in the lean phenotype

of our mice. Within and outside the brain, we predict that the

source of the insulin is critical for its effect on obesity.

ConclusionsWe generated mice that were genetically incapable of age-

dependent, diet-induced fasting hyperinsulinemia, and thereby

demonstrated that circulating insulin plays a causal role in

obesity in this model (Figure 7). We identify circulating insulin

as a critical regulator of Ucp1 expression in white adipose tissue

and whole-body energy expenditure. We find that increased fat

burning in white adipose tissue is associated with reduced lipid

(D) Immunoblot for Ucp1 in white adipose tissue. Lane 1 is brown adipose tissue

and Ponceau staining (pink). Quantification bars are the normalized ratio to b-ac

(E) Immunoblots of Pparg and Srebf1.

(F) Immunoblot analysis of Egr2.

*p < 0.05 versus high fat-fed Ins1+/+:Ins2�/� littermates (n = 4–5). Error bars repr

Cell M

accumulation in liver. Our data provide strong rationale to inves-

tigate approaches to limit peripheral hyperinsulinemia for the

prevention and treatment of obesity in mammals.

EXPERIMENTAL PROCEDURES

Experimental Animals and Glucose Homeostasis

Animal protocols were approved by the University of British Columbia Animal

Care Committee in accordance with national and international guidelines.

Ins1�/� and Ins2�/� mice were generated by the group of J. Jami (INSERM,

France), and their baseline phenotype described elsewhere (Duvillie et al.,

1997). C57Bl6/J mice (Jackson Laboratory, Bar Harbor, ME) were employed

for some studies, as indicated. Male mice were used unless otherwise noted.

Groups of mice were randomly divided into two diet groups at weaning

(3 weeks), and fed a control diet (25% fat) or a high-fat diet (58% fat). Additional

details of the diet composition can be found in the Supplemental Information.

Body weight, fasting glucose, glucose tolerance, and insulin tolerance were

examined after 4 hr fasts according to standard methods we have previously

described in more detail (Alejandro et al., 2011). The insulin-positive area was

used as an index of b cell mass, as described previously (Alejandro et al.,

2011). The cell death was assayed with the Roche TUNEL kit (Alejandro

et al., 2011). Serum insulin levels were measured with an ultrasensitive ELISA

kit (80-INSMSU-E01; ALPCO Diagnostics, Salem, NH). Leptin levels were

measured with mouse leptin ELISA kit (number 90030; CrystalChem, Downers

Grove, IL).

In Vivo Metabolic Analysis

PhenoMaster metabolic cages (TSE Systems, Chesterfield, MO) were used

for indirect calorimetry, activity, body weight, and food intake studies in

accordance with the manufacturer’s instructions. After 52 weeks of age,

mice were scanned for whole-body fat to lean mass ratio via NMR Spec-

troscopy at the 7T MRI Research Center (University of British Columbia,

Vancouver, Canada). Circulating fatty acids were measured with the Wako

NEFA kit.

Gene Expression and Protein Analysis

Mouse and human cDNA arrays, from tissue collected under IRB protocols,

were purchased from Origene (Rockville, MD). Gene expression analysis

was conducted with custom arrays of Taqman Real-Time PCR Assays (Life

Technologies, Burlington, Canada). Immunoblotting and staining were con-

ducted according to standard protocols (Alejandro et al., 2011). Brains were

frozen prior to cryosectioning, while all other tissues were fixed in 4% parafor-

maldehyde. Paraffin block sectioning and hemotoxyn and eosin staining were

conducted by the Histology Core at the Child and Family Research Institute

(Vancouver, Canada). The following antibodies were used in immunofluores-

cence studies of neurons and b cells: polyclonal rabbit anti-C-peptide-2 (Milli-

pore), monoclonal mouse anti-mature insulin (Biodesign), and polyclonal

guinea pig anti-(pan)insulin (Sigma). Uncoupling protein 1 protein levels were

quantified with antibodies from Santa Cruz (catalog number sc-6829), with

brown adipose tissue used as a positive control. Imageswere taken with either

an Olympus FV1000 confocal microscope or a Zeiss 200mmicroscope equip-

ped with deconvolution software (Slidebook, Intelligent Imaging Innovartions,

Denver, CO).

Statistical Analysis

Studies were repeated at least three times. All results are expressed asmean ±

SEM (indicated by error bars). Statistical analyses were performedwith Graph-

pad Prism 5 or Microsoft Excel. A student’s t test or AVOVA (Tukey’s) were

used as appropriate. A p value < 0.05 was considered significant.

(positive control). Equal protein loading was confirmed by b-actin immunoblot

tin (n = 4–5).

esent the SEM. See also Figure S5.


Figure 6. Mice with Reduced Ins1 Are Protected from Lipid Spill Over, Fatty Liver and ER Stress

(A) Circulating free fatty acids levels.

(B) Low and high magnification of hematoxylin and eosin-stained liver.

(C) Taqman real-time qPCR quantification of 49 genes in liver (n = 3–7 individual mice). Results are sorted as in Figure 3. #p < 0.05 (t test) between high fat-fed

Ins1+/�:Ins2�/� mice versus high fat-fed Ins1+/+:Ins2�/� mice. $Significant difference between diets within the Ins1+/�:Ins2�/� mice. &Differences between

Ins1+/�:Ins2�/� mice versus Ins1+/+:Ins2�/� mice on the control diet.

(D and E) Liver triglyceride and cholesterol measurements (n = 3–5).

(F and G) Markers of oxidative stress, protein carbonyl and 4-hydroxynonenal NHE, were measured in livers (n = 4–6). All data are from 1-year-old mice.

Error bars represent the SEM. See also Figure S5.

Cell Metabolism



Figure 7. Revisiting the Current Model of

Obesity and Type 2 Diabetes

(A) The most widely accepted model of the path-

ogenesis of obesity and type 2 diabetes posits that

a high-fat diet leads to obesity and insulin

resistance (there is debate about the relative order

and causality of these). In this widely held view,

insulin resistance then leads to hyperinsulinemia,

which is followed by b cell exhaustion, and then

type 2 diabetes. The accepted model is incom-

patible with our results that put the insulin hyper-

secretion genetically upstream of obesity.

(B) Our data support a model whereby insulin

levels must be kept low to maintain energy ex-

penditure in white adipose tissue via the expres-

sion of Ucp1. Our data do not address the order

of subsequent events after obesity (outside the

yellow box), such as insulin resistance and/or

type 2 diabetes, since they were not observed in

our studies. In other words, the effects of insulin

gene dosage on obesity are independent of sus-

tained changes in glucose homeostasis or insulin

resistance.

Cell Metabolism


SUPPLEMENTAL INFORMATION

Supplemental Information includes Supplemental Experimental Procedures

and five figures and can be found with this article online at http://dx.doi.org/

10.1016/j.cmet.2012.10.019.

ACKNOWLEDGMENTS

We thank Jacques Jami (INSERM) for giving us the insulin knockout mice. We

thank Jake Kushner (Baylor College of Medicine) for designing the Ins1 and

Ins2 Taqman primer sets and helpful discussions. We thank Sarah Gray

(University of Northern British Columbia), Rohit Kulkarni (Harvard University),

and Kenneth S. Polonsky (University of Chicago) for helpful comments on

early drafts of the manuscript. J.D.J. was supported by the JDRF, Canadian

Diabetes Association (CDA), Canadian Institutes for Health Research

(CIHR), and the Michael Smith Foundation for Health Research (MSFHR).

Cell M

T.J.K. was supported by CDA, CIHR, and MSFHR. S.M.C. was supported

by the CDA and the Heart and Stroke Foundation of BC and the Yukon.

N.M.T. was supported by the Natural Sciences and Engineering Research

Council of Canada.

Received: January 27, 2011

Revised: August 26, 2012

Accepted: October 31, 2012

Published: December 4, 2012

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Online Supplement

Hyperinsulinemia drives diet-induced obesity independently of brain insulin production

Arya E. Mehran1,2, Nicole M. Templeman1,2, G. Stefano Brigidi1, Gareth E. Lim1,2, Kwan-Yi Chu1,2,

Xiaoke Hu1,2, Jose Diego Botezelli1,2, Ali Asadi1,2, Bradford G. Hoffman2, Timothy J. Kieffer1,2, Shernaz

X. Bamji1, Susanne M. Clee1, and James D. Johnson1,2*

1 Department of Cellular and Physiological Sciences, University of British Columbia 2 Department of Surgery, University of British Columbia

SUPPLEMENTAL FIGURES

SUPPLEMENTAL FIGURE LEGENDS

Figure S1. Detection of Ins2/INSULIN mRNA and Ins2 enhancer activity in human and rodent

brain. (A) Sequencing-based analysis of the differential gene expression of Ins1 and Ins2 from 203

libraries representing 43 distinct tissue types. Only libraries where either insulin gene was found are

shown. Data are shown as the log 10 of the tags per million. The lightest yellow color is the log 10 of

7.4 tags per million (~0.87 tags per million) and the maximum red color is the log 10 of 224712 tpm

(~5.35). (B) Taqman real-time RT-qPCR data showing amplification human insulin from a commercial

panel of brain cDNAs (Origene). (C) Enhancer analysis of Ins1 and Ins2 loci in islets and brain. Data

representing the locations of enrichment for the transcription factors Pdx1, Foxa2, Mafa, and Neurod1

in pancreatic islets is shown on the UCSC genome browser views. Also shown are the locations of

histone H3 lysine 4 mono- and tri- methylation (H3K4me1 and H3K4me3 respectively), which are

associated with functional cis-regulatory regions, in islets (blue) and in cerebellum and cortex (grey).

In each case the higher the signal, or peak, the greater the level of enrichment of the factor, or histone

modification. From comparing the Ins1 and Ins2 loci, it is clear that Ins1 only has active histone

modifications (H3K4me1 and/or H3K4me3) enriched at its cis-regulatory loci in pancreatic islets. On

the other hand, Ins2 has H3K4me1 enrichment at its cis-regulatory loci in islets, cerebellum, and

cortex tissues, indicating it is under active cis-regulation in these tissues.

Figure S2. Validation of specific Ins1 and Ins2 mRNA and protein detection tools. (A) Raw

Taqman real-time RT-qPCR data showing specific amplification Ins1 (blue traces; absent in Ins1-/-

:Ins2+/+ mice) and Ins2 (red traces; absent in Ins1+/+:Ins2-/- mice) in isolated islets. Beta-actin (yellow

traces) is shown as a control. (B) Quantitative comparison of Ins2 mRNA levels in islets and brain

regions using Taqman RT-qPCR (red, hippocampus; orange, cortex; yellow, remainder of brain;

green, cerebellum; blue, olfactory bulb; purple, hypothalamus; in each case tissues were micro-

dissected from a single brain). Data are presented as relative mRNA amounts per isolated tissue (i.e.

not normalized for the amount of tissue/cells). Ins2 mRNA was serially diluted from a sample of 236

hand picked wildtype mouse islets to generate a standard curve. Thus, hippocampi from a single adult

wildtype mouse has less than 1,000 times the amount of insulin mRNA when compared with 236 islets

(with number of islets had roughly 8 times fewer cells than the hippocampi based on the nucleic acid

recovered from the samples). Based on published estimations each islet would be composed of 1260

!-cells (Jo et al., 2007) and each !-cell has ~50,000 copies of Ins2 mRNA. (C) Anti-C-peptide antibody

(Millipore # 4020-01) fails to stain Ins2 knockout islets, demonstrating its specificity for the Ins2 gene

product. (D) Confocal imaging of cultured hippocampal neurons from Ins1-/-:Ins2-/- mice show no C-

peptide 2 or (pan)insulin immunoreactivity. MAP3 staining (far red) and DAPI (blue) are used to

delineate individual neurons. (E) Pancreatic islets from Ins1-/-:Ins2+/+ mice express insulin but are

negative for !Gal, while Ins1-/-:Ins2-/- mice are negative for insulin but shown immunoreactivity for

!Gal. The Ins2 gene was replaced with the LacZ gene in our Ins2-/- mice.

Figure S3. Lack of fasting hyperinsulinemia and weight gain in high fat fed female Ins1+/+:Ins2-/-

and Ins1+/-:Ins2-/- mice. (A) Insulin, after a 4-hour fast, was measured in female mice at the ages

indicated (n=3). (B) Body weight was tracked weekly (n = 7-8 per group). (C) Glucose tolerance tests,

and area under the curve (insets) (n = 7-8). (D) Insulin tolerance tests, and area over the curve

(insets) (n = 7-8). * denotes significant differences. Error bars are standard error of the mean.

Figure S4. Correlation between fasting insulin and body weight in young mice within

genotypes. (A,B) Plasma insulin and glucose after a 4-hour fast in an independent cohort of young

mice. (C) Body weight in young male Ins1+/+:Ins2-/- mice and Ins1+/-:Ins2-/- mice fed a high fat diet from

4 weeks of age. (D) A significant correlation between body mass and fasting insulin is maintained

within genotypes in 9-week old mice, at a time when fasting insulin begins to diverge, but prior to

significant differences in body mass between groups (n = 9-10). Error bars are standard error of the

mean.

Figure S5. Quantitative gene expression profile of brown adipose tissue and skeletal muscle.

Taqman qPCR of intra-scapular brown adipose tissue (A) and gastrocnemius skeletal muscle (B) in

high fat fed Ins1+/+:Ins2-/- and Ins1+/-:Ins2-/- mice. Data are ranked by numerical differences between

conditions. No statistical significances were detected (n = 7,5).

SUPPLEMENTAL METHODS

Experimental animals

Ins1-/- and Ins2-/- mice were previously generated and their phenotype described in young mice fed

a normal diet (Duvillie et al., 1997). Ins1 was disrupted and replaced by a neomycin resistance

cassette. Most of the Ins2 sequence was replaced by a LacZ/neo cassette (Duvillie et al., 1997). For

genotyping, tail samples were collected and the DNA was isolated using DNeasy Blood and Tissue Kit

(Qiagen). Groups of mice were divided into two diet groups at weaning (3 weeks), one group was kept

on the control diet (total calories = 4.68 kcal/g; 25.3% calories from fat, 19.8% calories from protein,

54.9% calories from carbohydrate; Catalog #5015 Lab Diets, Richmond, IN) and the other group was

put on a high-fat diet (total calories = 5.56 kcal/g; 58.0% calories from fat, 16.4% calories from protein,

25.5% calories from carbohydrate; Catalog #D12330 Open Source Diets/Research Diets, New

Brunswick, NJ). After 52 weeks of age, mice were scanned for whole body fat to lean mass ratio using

NMR Spectroscopy at the 7T MRI Research Center (University of British Columbia, Vancouver, BC).

Glucose tolerance, insulin tolerance, and hormone secretion

Body weight and basal glucose (OneTouch glucose meters, LifeScan Canada, Burnaby, BC) were

examined weekly after a 4-hour fast. Glucose tolerance was examined after intraperitoneal injection

with 11.1 !L per gram of body weight of 18% glucose in 0.9% NaCl saline. Insulin tolerance test was

assessed after injecting 0.75 U of insulin (Lispro Humalog VL-7510 in 0.9% NaCl solution) per gram of

body weight. Serum insulin levels were measured using ultrasensitive mouse insulin ELISA kit (80-

INSMSU-E01; ALPCO Diagnostics, Salem, NH) and leptin levels were measured using mouse leptin

ELISA kit (90030) from CrystalChem Inc. (Downers Grove, IL).

Metabolic Cage Analysis

At 8 and 20-22 weeks of age, mice (n = 3-5 per genotype) were individually housed in

PhenoMaster metabolic cages (TSE Systems Inc., Chesterfield MO) for indirect calorimetry. The

cages were also equipped with food, drink and body weight monitors and an infrared beam grid to

monitor activity in the x, y and z axes. Cages were placed in an environmental chamber to maintain

constant temperature (21°C), with room lighting cycles (12 hour light, 7 am - 7 pm). Animals remained

in the cages for 72 horus. Data from the first 4 hours were not used in analysis, to allow for

acclimatization. Results from each of the 3 days were averaged and presented as a prototypical day

for each genotype.

Tissue Collection and Analysis

Mice were euthanized and tissues were collected and some samples were frozen instantly in liquid

nitrogen and transferred to -80°C freezer for storage, while other samples were fixed in 4%

paraformaldehyde for tissue sectioning. Tissues collected were as follows: pancreas, epididymal fat

pads, calf muscle, liver, brain, kidney, spleen, heart, thymus and tibia. Tibias were put in 2% KOH for

removal of non-bone tissue for physical measurements. Serial paraffin sections were made at 5 !m

thickness. Tissue sections were prepared at Child and Family Research Institute Histology Core

Facility (Vancouver, BC).

To determine the total cholesterol and triglyceride content, 100 mg of liver was homogenized using

a Tissue-Tearor (Biospec) in 2.5 ml of chloroform/methanol solution (2:1). Then, 1.5 ml of ice-cold

distilled deionized water was added to the samples, vortexed briefly, and centrifuged for 10 min at

3800 RPM. Supernatants were disposed and another 1.5 ml of ice-cold water was added. The last

step was repeated. 500 µl of the lower phase of the samples and the standards were dried in N2 gas,

afterwhich 10 µl of Thesit (Sigma-Aldrich®) and 100 µl of water were added. Assays were performed

using commercially available kits for triglycerides (Sigma) and total cholesterol (Wako).

Pancreatic islet morphology and hormone expression were approximated using the insulin positive

area from tissue sections 200 !m apart stained with guinea pig anti-insulin and rabbit anti glucagon

(Linco/Millipore). Sections were mounted in Vectashield solution with DAPI (Reactolab SA,

Switzerland) and imaged through a Zeiss 10x objective, individual filter cubes for each color, and a

CoolSnap HQ2 Camera (Roper Scientific). Images were analyzed using Slidebook software

(Intelligent Imaging Innovations) as previously described (Jeffrey et al., 2008).

For the analysis of cultured hippocampal neurons, pregnant mice or rats were euthanized 1 day

before timed birth and their hippocampi collected, cultured and fixed using paraformaldehyde. Slides

were stained with rabbit anti-C-peptide 2 which would be expected to recognize C-peptide 2 as well as

proinsulin 2 (Millipore catalog # 4020-01). We have confirmed independently, using islets from our

knockout mice as negative controls, that this antibody specifically recognizes the C-peptide of Insulin 2

but not Insulin 1 (Fig. S1B). In some studies, we also employed an antibody that only recognizes

mature insulin (Biodesign, mAB1)(Jeffrey et al., 2008) and a guinea pig antibody expected to

recognize mature and immature forms of insulin (Sigma). In order to determine whether insulin was

being produced by these neurons, we marked the endoplasmic reticulum using a calnexin antibody

(Sigma). Neurons were marked with a mouse anti-NeuN antibody, MAP2 antibody, or identified

morphologically after transfection with GFP. Uncoupling protein 1 protein levels were quantified using

standard immunoblotting techniques, as previously detailed (Jeffrey et al., 2008) and antibodies from

Santa Cruz (Catalog # UCP1 M-17 sc-6829) or generated in house.

Analysis of Insulin Gene Expression

Total RNA was isolated using the RNeasy Mini Kit (Qiagen, Biosciences). cDNA was synthesized

using qScript cDNA synthesis kit (Quanta, Biosciences). cDNA (RNeasy kit, Qiagen Biosciences;

qScript kit, Quanta Biosciences) was subjected to Taqman real-time PCR (StepOnePlus, Applied

Biosystems). PerfeCta qPCR supermix (Quanta, Biosciences) was used to perform the PCR in

StepOnePlus real-time PCR system (Applied Biosystems). Following primers were used Ins1 Taqman

forward 169!189: GAA GTG GAG GAC CCA CAA GTG, Ins1 Taqman reverse 273!254: ATC CAC

AAT GCC ACG CTT CT, Ins1 Taqman probe 219!200: CCC GGG GCT TCC TCC CAG CT, Ins2

taqman forward 169!189: GAA GTG GAG GAC CCA CAA GTG, Ins2 Taqman reverse 280!259:

GAT CTA CAA TGC CAC GCT TCT G, Ins2 Taqman probe 224!207: CCT GCT CCC GGG CCT

CCA. PCR conditions were 2 min at 50 °C, 10 min at 95 °C and followed by 40 cycles of 15 sec at 95

°C, 1 min at 60 °C. Reverse log of the raw values were used for analysis. Mouse brain cDNA panel

(MDRT101) and human brain cDNA panel (HBRT101) were purchased from Origene (Rockville, MD).

Human INSULIN probe set (Hs00355773-m1) was from Applied Biosystems.

Analysis of sequencing-based gene expression data was performed as described previously

(Hoffman et al., 2008). Briefly, available serial analysis of gene expression (SAGE) data from 205

different mouse libraries, generated as part of the Mouse Atlas of Gene Expression (Hoffman et al.,

2008; Siddiqui et al., 2005), was obtained (http://cgap.nci.nih.gov/SAGE). Data were then filtered for

sequence quality so that each tag had a 95% or greater probability of being correct, using PHRED

score quality assessment software. The counts of tags corresponding Ins1 and Ins2 was then

determined in each of the 205 libraries. Tags corresponding to Ins1 were detected in 7 out of 205

libraries, while tags corresponding to Insulin 2 were detected in 20 out of 205 libraries (Figure 1C).

Data are shown as the log 10 of the tags per million. The lightest blue color is the log 10 of 7.4 tags

per million (~0.87) and the max color is the log 10 of 224712 tpm (~5.35).

To complement our own investigations, we also performed an exhaustive literature search and

found over 80 reports of CNS insulin expression in the CNS of various species (e.g. Broughton and

Partridge, 2009; Broughton et al., 2010; Clarke et al., 1986; Deltour et al., 1993; Devaskar, 1991;

Devaskar et al., 1994; Devaskar et al., 1993; Dorn et al., 1981; Fan et al., 2009; Fan et al., 2010;

Frolich et al., 1998; Frolich et al., 1999; Giddings et al., 1985; Grunblatt et al., 2006; Grunblatt et al.,

2007; Hrytsenko et al., 2007; Hua et al., 2003; Hwangbo et al., 2004; Kodama et al., 2006; Lamotte et

al., 2004; Nakayama et al., 2005; Narasimhan et al., 2009; Oda et al., 2011; Osmanovic et al., 2010;

Plaschke et al., 2009, 2010; Pugliese et al., 1997; Riederer et al., 2011; Rulifson et al., 2002; Salkovic-

Petrisic et al., 2005; Salkovic-Petrisic et al., 2009; Schechter et al., 1988; Schechter et al., 1994;

Serrano et al., 1989; Singh et al., 1997; Steen et al., 2005; You et al., 2008). In rodents, this evidence

includes Ins2 promoter activity(Alpert et al., 1988; Lamotte et al., 2004), mRNA (Northern, RT-PCR,

real-time RT-qPCR, in situ hybridization)(Cohen et al., 2007; de la Monte, 2009; de la Monte et al.,

2011; de la Monte et al., 2008; de la Monte et al., 2006; de la Monte and Wands, 2005, 2008; de la

Monte et al., 2005; Deltour et al., 1993; Devaskar, 1991; Devaskar et al., 1993; Giddings et al., 1985;

Grunblatt et al., 2006; Grunblatt et al., 2007; Havrankova et al., 1978a, b; Jones et al., 2011; Kojima et

al., 2006; Lamotte et al., 2004; Lester-Coll et al., 2006; Moroz et al., 2008; Osmanovic et al., 2010;

Plaschke et al., 2010; Riederer et al., 2011; Rivera et al., 2005; Salkovic-Petrisic et al., 2005; Salkovic-

Petrisic et al., 2009; Schechter et al., 1990; Schechter et al., 1992; Schechter et al., 1994; Singh et al.,

1997; Soscia et al., 2006; Wozniak et al., 1993; Young, 1986), and protein (immunohistochemistry,

RIA, HPLC)(Bernstein et al., 1984; Birch et al., 1984a, b, 1987; Clarke et al., 1986; de la Monte, 2009;

de la Monte and Wands, 2008; Devaskar, 1991; Devaskar et al., 1994; Devaskar et al., 1993; Dheen

et al., 1996; Dorn et al., 1984a; Dorn et al., 1981; Dorn et al., 1980; Dorn et al., 1982a; Dorn et al.,

1983a; Dorn et al., 1983b; Dorn et al., 1982b; Dorn et al., 1984b; Havrankova et al., 1978a, b; Hill et

al., 1986; Kojima et al., 2006; Nishimura et al., 1991; Raizada, 1983; Riederer et al., 2011;

Rosenzweig et al., 1980; Schechter et al., 1988; Schechter et al., 1990; Schechter et al., 1994; Singh

et al., 1997; Stempniak and Guzek, 1993; Stevenson, 1983; Unger et al., 1989; Weyhenmeyer and

Fellows, 1983; Wozniak et al., 1993). Animals with a single insulin gene (rabbits, guinea pigs, dogs,

non-human primates) also have brain insulin(Watanabe et al., 1986), suggesting a default pattern

(rodent Ins1 being the exception). Multiple groups have shown that primary neuronal cultures secrete

metabolically labeled, newly synthesized insulin into conditioned media (Birch et al., 1984a; Devaskar

et al., 1994). Insulin levels are highest during development and decline after birth, until insulin can only

be found in rare cells, (Bernstein et al., 1984), like many other neuropeptides (Albertson et al., 2008;

Najimi et al., 1990). Multiple laboratories, using different antibodies, have found diffuse insulin staining

in the same areas of the brain, including the hypothalamus, thalamus, amygdala and prominently in

the hippocampus (Bernstein et al., 1984; Dorn et al., 1984a; Dorn et al., 1981; Dorn et al., 1980; Dorn

et al., 1982a; Dorn et al., 1983a; Dorn et al., 1983b; Dorn et al., 1982b; Dorn et al., 1984b). Neuronal

insulin expression and staining are expected to be much weaker than in beta-cells because local

action does not require ‘endocrine’ levels of peptide production in each cell, just enough so that

neighboring cells can sense and respond with amplifying signalling cascades. Insulin receptors

respond to picomolar levels of the hormone (Alejandro et al., 2010; Beith et al., 2008; Johnson and

Alejandro, 2008; Johnson et al., 2006a; Johnson et al., 2006b; Johnson and Misler, 2002; Luciani and

Johnson, 2005). Importantly, there is no evidence that insulin produced in the brain has any effect on

circulating peripheral insulin, suggesting that it must act locally. Central glucose infusion or feeding

rapidly increase extracellular insulin in the hypothalamus, but does not affect peripheral insulin levels

or glycemia (Gerozissis et al., 1993, 1997; Gerozissis et al., 2001; Gerozissis et al., 1999; Orosco et

al., 1995a; Orosco et al., 1995b; Orosco et al., 2000). Indeed, brain insulin mRNA levels are dynamic,

and are modulated positively and negatively by a large number of factors (de la Monte, 2009; de la

Monte et al., 2011; de la Monte et al., 2008; de la Monte et al., 2006; de la Monte et al., 2010; de la

Monte and Wands, 2002, 2005, 2008; de la Monte et al., 2005; Lester-Coll et al., 2006; Soscia et al.,

2006; Tong and de la Monte, 2009; Tong et al., 2009), further suggesting that it is not artifactual.

Analysis of putative insulin target genes

Analysis of gene expression patterns in white adipose tissue, liver, and skeletal muscle was

performed using custom Taqman mini-arrays. Each 96-well PCR plate was divided in half with 45

genes of interest and 3 internal controls (18S, "-actin/Actb, hypoxanthine guanine phosphoribosyl

transferase 1/Hprt1). Genotypes were then compared on the same plate, side-by-side. We chose

genes involved in lipid metabolism, several of which are known to be insulin target genes, including:

ATP citrate lyase (Acly)(Chu et al., 2010; Pearce et al., 1998), hormone sensitive lipase (Hsl)(Harada

et al., 2003; Langin et al., 1993), fatty acid synthase (Fasn)(Moon et al., 2000; Wang and Sul, 1995,

1998), patatin-like phospholipase domain containing 2 (Atgl)(Lake et al., 2005; Zimmermann et al.,

2004), patatin-like phospholipase domain containing 3 (Adpn)(Huong and Ide, 2008; Kershaw et al.,

2006; Lake et al., 2005; Liu et al., 2004; Moldes et al., 2006), acetyl coa carboxylase (Acacb)(Czech et

al., 1989) and stearoyl-coenzyme a desaturase 1 (Scd1)(Brown et al., 2008; Flowers and Ntambi,

2008; Mauvoisin and Mounier, 2011; Prasad and Joshi, 1979; Rahman et al., 2003). We chose to

examine each of the uncoupling proteins (Ucp1, Ucp2, Ucp3)(Alemzadeh et al., 2008; Almind et al.,

2007; Azzu and Brand, 2010; Bhopal and Rafnsson, 2009; Chan and Harper, 2006; Christian et al.,

2005; Jezek, 2002; Krauss et al., 2005; Mogensen et al., 2007; Zhang et al., 2001). A number of

transcription factors involved in regulating metabolism were chosen: peroxisome proliferator activated

receptor alpha and gamma (Ppara, Pparg)(Finck et al., 2006; Guilherme et al., 2008; Jay and Ren,

2007; Jones et al., 2005; Tontonoz and Spiegelman, 2008), peroxisome proliferative gamma

coactivator 1 alpha (Ppargc1a)(Liu et al., 2008; Puigserver et al., 1999; Wu et al., 1999),

CCAAT/enhancer binding protein (Cebpa)(Miller et al., 1996), sterol regulatory element binding

transcription factor 1 (srebf1, also known as Srebp1c)(Kim et al., 1998; Lin et al., 2005), mlx

interacting protein-like (Chrebp)(Ferre and Foufelle, 2010; Iizuka and Horikawa, 2008; Miyazaki et al.,

2007; Postic et al., 2007; Sanchez et al., 2009; Sirek et al., 2009; Wong and Sul, 2010), forkhead box

a2 (Foxa2)(Puigserver and Rodgers, 2006; Silva et al., 2009; Tabassum et al., 2008; Wolfrum et al.,

2004; Wolfrum and Stoffel, 2006), forkhead box c2 (Foxc2)(Cederberg et al., 2001; Di Gregorio et al.,

2004; Gronning et al., 2002; Lidell et al., 2011; Yang et al., 2003), forkhead box o1 (Foxo1)(Farmer,

2003; Liu et al., 2008; Matsuzaki et al., 2003; Nakae et al., 2003; Puigserver et al., 2003), sirtuin 1

(Sirt1)(Bai et al., 2011; Coppari et al., 2009; Iwabu et al., 2010; Kaestner, 2008; Lagouge et al., 2006;

Liu et al., 2008; Luo and Kraus, 2011; Purushotham et al., 2009), nuclear receptor subfamily 1

(Lxr)(Baranowski, 2008; Fernandez-Veledo et al., 2006; Kalaany et al., 2005; Laffitte et al., 2003;

Mukherjee et al., 1997), early growth response 1 (Egr1)(Jhun et al., 1995; Keeton et al., 2003; Nelson

et al., 2011; Shen et al., 2011), early growth response 2 (Egr2)(Keeton et al., 2003; Li et al., 2010;

Wang et al., 2009), kruppel-like factor 15 (Klf15)(Elbein et al., 2011; Gray et al., 2002; Teshigawara et

al., 2005; Yamamoto et al., 2004), nuclear receptor interacting protein 1 (Rip140)(Christian et al.,

2005; Leonardsson et al., 2004), as well as the stress factors, activating transcription factor 3

(Atf3)(Gilchrist et al., 2006; Kim et al., 2006; Lim et al., 2006; Qi et al., 2009; Zmuda et al., 2010), and

DNA-damage inducible transcript 3 (Ddit3, also known as Chop)(Ariyama et al., 2007; Ozcan et al.,

2004). We also examined genes involved in insulin signaling such as insulin receptor (Insr)(Bluher et

al., 2003; Bluher et al., 2002; Bruning et al., 2000; Saltiel and Kahn, 2001), protein tyrosine

phosphatase (Ptpn1)(Koren and Fantus, 2007), and solute carrier family 2 member 4 (Glut4)(Gray et

al., 2002; Miller et al., 2007; Park et al., 2005; Qi et al., 2009). Some adipokines and their receptors

were also tested: adiponectin (Adipoq), adiponectin receptor 1 (Adipor1), adiponectin receptor 2

(Adipor2)(Ahima and Lazar, 2008; Koh et al., 2010; Liu et al., 2007), leptin (Lep) and leptin receptor

(Lepr)(Ahima and Lazar, 2008; Anubhuti and Arora, 2008; Das, 2010). Inflammatory factors that are

known to be heightened in obesity were also tested, including the macrophage marker F4/80 (EGF-

like module-containing mucin-like hormone receptor-like 1, Emr1), interleukin 6 (IL6) and tumor

necrosis factor alpha (TNFa)(Guilherme et al., 2008; Pradhan, 2007). Other genes that are reported to

be involved in obesity were chosen, including alpha-ketoglutarate-dependent dioxygenase (Fto)(Dina

et al., 2007; Frayling et al., 2007), insulin-like growth factor binding protein 1 (Igfbp1)(Wheatcroft and

Kearney, 2009), fibroblast growth factor 21 (Fgf21)(Kharitonenkov et al., 2005), and retinol binding

protein 4 (Rbp4)(Graham et al., 2006; Kloting et al., 2007; Yang et al., 2005).

Statistical and Data Analysis.

All results are expressed as means ± SEM. For glucose tolerance, insulin tolerance and insulin

secretion testes statistical analysis was performed on values obtained from measuring area under the

curve (AUC). Statistical analysis was performed using Prism 5 (Graphpad). Two way ANOVA or one-

way ANOVA followed by Tukey's HSD was used where appropriate. P < 0.05 was considered to be

significant.

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Hyperinsulinemia Drives Diet-Induced Obesity Independently of Brain Insulin Production

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